Having covered the primary categories of meta-learning algorithms, specific adaptation techniques for foundation models, and the practicalities of scaling these methods, this chapter examines several specialized areas and the theoretical foundations supporting meta-learning.

You will learn about:

• Probabilistic approaches to meta-learning using Bayesian methods, enabling uncertainty quantification in adaptation.
• Strategies for continual meta-learning, where models adapt to sequences of tasks without forgetting previous ones.
• The application of meta-learning principles within reinforcement learning (Meta-RL) to accelerate policy acquisition.
• Theoretical analyses concerning generalization bounds in meta-learning, aiming to understand how performance on meta-training tasks translates to new, unseen tasks.
• Connections between meta-learning and information theory, providing different perspectives on the learning process.
• Current open problems and research directions, highlighting areas needing further investigation, particularly concerning large-scale models.

This chapter provides a broader perspective on the meta-learning field, connecting practical algorithms to their theoretical underpinnings and exploring active research frontiers.